Articles | Volume 16, issue 2
https://doi.org/10.5194/tc-16-625-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/tc-16-625-2022
© Author(s) 2022. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
18 Feb 2022
Research article |
| 18 Feb 2022
Sentinel-1 time series for mapping snow cover depletion and timing of snowmelt in Arctic periglacial environments: case study from Zackenberg and Kobbefjord, Greenland
Sebastian Buchelt
CORRESPONDING AUTHOR
Department Physical Geography, Institute of Geography and Geology, University of Würzburg, 97074 Würzburg, Germany
Kirstine Skov
Department of Bioscience, Arctic Research Center, Aarhus University, 4000 Roskilde, Denmark
Kerstin Krøier Rasmussen
Asiaq, Nuuk, Greenland
Tobias Ullmann
Department Physical Geography, Institute of Geography and Geology, University of Würzburg, 97074 Würzburg, Germany
Related authors
Johannes Buckel, Jan Mudler, Rainer Gardeweg, Christian Hauck, Christin Hilbich, Regula Frauenfelder, Christof Kneisel, Sebastian Buchelt, Jan Henrik Blöthe, Andreas Hördt, and Matthias Bücker
The Cryosphere, 17, 2919–2940, https://doi.org/10.5194/tc-17-2919-2023, https://doi.org/10.5194/tc-17-2919-2023, 2023
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This study reveals permafrost degradation by repeating old geophysical measurements at three Alpine sites. The compared data indicate that ice-poor permafrost is highly affected by temperature warming. The melting of ice-rich permafrost could not be identified. However, complex geomorphic processes are responsible for this rather than external temperature change. We suspect permafrost degradation here as well. In addition, we introduce a new current injection method for data acquisition.
Johannes Buckel, Jan Mudler, Rainer Gardeweg, Christian Hauck, Christin Hilbich, Regula Frauenfelder, Christof Kneisel, Sebastian Buchelt, Jan Henrik Blöthe, Andreas Hördt, and Matthias Bücker
The Cryosphere, 17, 2919–2940, https://doi.org/10.5194/tc-17-2919-2023, https://doi.org/10.5194/tc-17-2919-2023, 2023
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This study reveals permafrost degradation by repeating old geophysical measurements at three Alpine sites. The compared data indicate that ice-poor permafrost is highly affected by temperature warming. The melting of ice-rich permafrost could not be identified. However, complex geomorphic processes are responsible for this rather than external temperature change. We suspect permafrost degradation here as well. In addition, we introduce a new current injection method for data acquisition.
Tobias Ullmann, Eric Möller, Roland Baumhauer, Eva Lange-Athinodorou, and Julia Meister
E&G Quaternary Sci. J., 71, 243–247, https://doi.org/10.5194/egqsj-71-243-2022, https://doi.org/10.5194/egqsj-71-243-2022, 2022
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In this contribution we highlight as an example the application of a freely available tool for the Google Earth Engine. The software allows cloud-free satellite images to be processed. We show processing examples for the Nile Delta (Egypt) and how the remote sensing images are used to find hints of buried landforms, such as former river branches of the Nile.
Julia Meister, Eva Lange-Athinodorou, and Tobias Ullmann
E&G Quaternary Sci. J., 70, 187–190, https://doi.org/10.5194/egqsj-70-187-2021, https://doi.org/10.5194/egqsj-70-187-2021, 2021
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This is the preface to the special issue "Geoarchaeology of the Nile Delta: Current Research and Future Prospects", which brings together geoarchaeological case studies from different regions of the Nile Delta.
Tobias Ullmann, Leon Nill, Robert Schiestl, Julian Trappe, Eva Lange-Athinodorou, Roland Baumhauer, and Julia Meister
E&G Quaternary Sci. J., 69, 225–245, https://doi.org/10.5194/egqsj-69-225-2020, https://doi.org/10.5194/egqsj-69-225-2020, 2020
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The contribution highlights the use of Landsat archive data (1985–2019) for the detection of surface anomalies potentially related to buried near-surface paleogeomorphological deposits in the Nile Delta (Egypt). The analyses of selected spectral-temporal metrics showed several anomalies in the immediate surroundings of Pleistocene sand hills (geziras) and settlement mounds (tells) of the eastern Delta, which allowed mapping of the potential near-surface continuation.
Related subject area
Discipline: Snow | Subject: Remote Sensing
Evaluation of snow depth retrievals from ICESat-2 using airborne laser-scanning data
How do tradeoffs in satellite spatial and temporal resolution impact snow water equivalent reconstruction?
Exploring the use of multi-source high-resolution satellite data for snow water equivalent reconstruction over mountainous catchments
Estimating snow accumulation and ablation with L-band interferometric synthetic aperture radar (InSAR)
Snowmelt characterization from optical and synthetic-aperture radar observations in the La Joie Basin, British Columbia
Topographic and vegetation controls of the spatial distribution of snow depth in agro-forested environments by UAV lidar
Temporal stability of long-term satellite and reanalysis products to monitor snow cover trends
Towards long-term records of rain-on-snow events across the Arctic from satellite data
Implementing spatially and temporally varying snow densities into the GlobSnow snow water equivalent retrieval
Evaluation of E3SM land model snow simulations over the western United States
Landsat, MODIS, and VIIRS snow cover mapping algorithm performance as validated by airborne lidar datasets
Snow stratigraphy observations from Operation IceBridge surveys in Alaska using S and C band airborne ultra-wideband FMCW (frequency-modulated continuous wave) radar
Measuring the spatiotemporal variability of snow depth in subarctic environments using unmanned aircraft systems (UAS) – Part 2: Snow processes and snow-canopy interactions
Measuring the spatiotemporal variability of snow depth in subarctic environments using unmanned aircraft systems (UAS) – Part 1: Measurements, processing, and accuracy assessment
Evaluating the Utility of Active Microwave Observations as a Snow Mission Concept Using Observing System Simulation Experiments
Brief communication: A continuous formulation of microwave scattering from fresh snow to bubbly ice from first principles
Review article: Global monitoring of snow water equivalent using high-frequency radar remote sensing
Automated avalanche mapping from SPOT 6/7 satellite imagery with deep learning: results, evaluation, potential and limitations
Potential of X-band polarimetric synthetic aperture radar co-polar phase difference for arctic snow depth estimation
Snow water equivalent change mapping from slope-correlated synthetic aperture radar interferometry (InSAR) phase variations
Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps
Characterizing tundra snow sub-pixel variability to improve brightness temperature estimation in satellite SWE retrievals
Mapping liquid water content in snow at the millimeter scale: an intercomparison of mixed-phase optical property models using hyperspectral imaging and in situ measurements
Brief communication: Evaluation of the snow cover detection in the Copernicus High Resolution Snow & Ice Monitoring Service
Evaluation of snow extent time series derived from Advanced Very High Resolution Radiometer global area coverage data (1982–2018) in the Hindu Kush Himalayas
Deriving Arctic 2 m air temperatures over snow and ice from satellite surface temperature measurements
Impact of dynamic snow density on GlobSnow snow water equivalent retrieval accuracy
The retrieval of snow properties from SLSTR Sentinel-3 – Part 1: Method description and sensitivity study
The retrieval of snow properties from SLSTR Sentinel-3 – Part 2: Results and validation
Tree canopy and snow depth relationships at fine scales with terrestrial laser scanning
Snow depth mapping with unpiloted aerial system lidar observations: a case study in Durham, New Hampshire, United States
Mapping avalanches with satellites – evaluation of performance and completeness
Estimating fractional snow cover from passive microwave brightness temperature data using MODIS snow cover product over North America
Snow depth time series retrieval by time-lapse photography: Finnish and Italian case studies
Intercomparison of photogrammetric platforms for spatially continuous snow depth mapping
Simulating optical top-of-atmosphere radiance satellite images over snow-covered rugged terrain
Parameterizing anisotropic reflectance of snow surfaces from airborne digital camera observations in Antarctica
Snow depth mapping from stereo satellite imagery in mountainous terrain: evaluation using airborne laser-scanning data
Improving sub-canopy snow depth mapping with unmanned aerial vehicles: lidar versus structure-from-motion techniques
Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions
Comparison of modeled snow properties in Afghanistan, Pakistan, and Tajikistan
Effect of snow microstructure variability on Ku-band radar snow water equivalent retrievals
Regional influence of ocean–atmosphere teleconnections on the timing and duration of MODIS-derived snow cover in British Columbia, Canada
Estimating snow depth on Arctic sea ice using satellite microwave radiometry and a neural network
Suitability analysis of ski areas in China: an integrated study based on natural and socioeconomic conditions
Estimating snow depth over Arctic sea ice from calibrated dual-frequency radar freeboards
Monitoring snow depth change across a range of landscapes with ephemeral snowpacks using structure from motion applied to lightweight unmanned aerial vehicle videos
Repeat mapping of snow depth across an alpine catchment with RPAS photogrammetry
On the reflectance spectroscopy of snow
On the need for a time- and location-dependent estimation of the NDSI threshold value for reducing existing uncertainties in snow cover maps at different scales
César Deschamps-Berger, Simon Gascoin, David Shean, Hannah Besso, Ambroise Guiot, and Juan Ignacio López-Moreno
The Cryosphere, 17, 2779–2792, https://doi.org/10.5194/tc-17-2779-2023, https://doi.org/10.5194/tc-17-2779-2023, 2023
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The estimation of the snow depth in mountains is hard, despite the importance of the snowpack for human societies and ecosystems. We measured the snow depth in mountains by comparing the elevation of points measured with snow from the high-precision altimetric satellite ICESat-2 to the elevation without snow from various sources. Snow depths derived only from ICESat-2 were too sparse, but using external airborne/satellite products results in spatially richer and sufficiently precise snow depths.
Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
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To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Valentina Premier, Carlo Marin, Giacomo Bertoldi, Riccardo Barella, Claudia Notarnicola, and Lorenzo Bruzzone
The Cryosphere, 17, 2387–2407, https://doi.org/10.5194/tc-17-2387-2023, https://doi.org/10.5194/tc-17-2387-2023, 2023
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The large amount of information regularly acquired by satellites can provide important information about SWE. We explore the use of multi-source satellite data, in situ observations, and a degree-day model to reconstruct daily SWE at 25 m. The results show spatial patterns that are consistent with the topographical features as well as with a reference product. Being able to also reproduce interannual variability, the method has great potential for hydrological and ecological applications.
Jack Tarricone, Ryan W. Webb, Hans-Peter Marshall, Anne W. Nolin, and Franz J. Meyer
The Cryosphere, 17, 1997–2019, https://doi.org/10.5194/tc-17-1997-2023, https://doi.org/10.5194/tc-17-1997-2023, 2023
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Mountain snowmelt provides water for billions of people across the globe. Despite its importance, we cannot currently measure the amount of water in mountain snowpacks from satellites. In this research, we test the ability of an experimental snow remote sensing technique from an airplane in preparation for the same sensor being launched on a future NASA satellite. We found that the method worked better than expected for estimating important snowpack properties.
Sara E. Darychuk, Joseph M. Shea, Brian Menounos, Anna Chesnokova, Georg Jost, and Frank Weber
The Cryosphere, 17, 1457–1473, https://doi.org/10.5194/tc-17-1457-2023, https://doi.org/10.5194/tc-17-1457-2023, 2023
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We use synthetic-aperture radar (SAR) and optical observations to map snowmelt timing and duration on the watershed scale. We found that Sentinel-1 SAR time series can be used to approximate snowmelt onset over diverse terrain and land cover types, and we present a low-cost workflow for SAR processing over large, mountainous regions. Our approach provides spatially distributed observations of the snowpack necessary for model calibration and can be used to monitor snowmelt in ungauged basins.
Vasana Dharmadasa, Christophe Kinnard, and Michel Baraër
The Cryosphere, 17, 1225–1246, https://doi.org/10.5194/tc-17-1225-2023, https://doi.org/10.5194/tc-17-1225-2023, 2023
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This study highlights the successful usage of UAV lidar to monitor small-scale snow depth distribution. Our results show that underlying topography and wind redistribution of snow along forest edges govern the snow depth variability at agro-forested sites, while forest structure variability dominates snow depth variability in the coniferous environment. This emphasizes the importance of including and better representing these processes in physically based models for accurate snowpack estimates.
Ruben Urraca and Nadine Gobron
The Cryosphere, 17, 1023–1052, https://doi.org/10.5194/tc-17-1023-2023, https://doi.org/10.5194/tc-17-1023-2023, 2023
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We evaluate the fitness of some of the longest satellite (NOAA CDR, 1966–2020) and reanalysis (ERA5, 1950–2020; ERA5-Land, 1950–2020) products currently available to monitor the Northern Hemisphere snow cover trends using 527 stations as the reference. We found different artificial trends and stepwise discontinuities in all the products that hinder the accurate monitoring of snow trends, at least without bias correction. The study also provides updates on the snow cover trends during 1950–2020.
Annett Bartsch, Helena Bergstedt, Georg Pointner, Xaver Muri, Kimmo Rautiainen, Leena Leppänen, Kyle Joly, Aleksandr Sokolov, Pavel Orekhov, Dorothee Ehrich, and Eeva Mariatta Soininen
The Cryosphere, 17, 889–915, https://doi.org/10.5194/tc-17-889-2023, https://doi.org/10.5194/tc-17-889-2023, 2023
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Rain-on-snow (ROS) events occur across many regions of the terrestrial Arctic in mid-winter. In extreme cases ice layers form which affect wildlife, vegetation and soils beyond the duration of the event. The fusion of multiple types of microwave satellite observations is suggested for the creation of a climate data record. Retrieval is most robust in the tundra biome, where records can be used to identify extremes and the results can be applied to impact studies at regional scale.
Pinja Venäläinen, Kari Luojus, Colleen Mortimer, Juha Lemmetyinen, Jouni Pulliainen, Matias Takala, Mikko Moisander, and Lina Zschenderlein
The Cryosphere, 17, 719–736, https://doi.org/10.5194/tc-17-719-2023, https://doi.org/10.5194/tc-17-719-2023, 2023
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Snow water equivalent (SWE) is a valuable characteristic of snow cover. In this research, we improve the radiometer-based GlobSnow SWE retrieval methodology by implementing spatially and temporally varying snow densities into the retrieval procedure. In addition to improving the accuracy of SWE retrieval, varying snow densities were found to improve the magnitude and seasonal evolution of the Northern Hemisphere snow mass estimate compared to the baseline product.
Dalei Hao, Gautam Bisht, Karl Rittger, Timbo Stillinger, Edward Bair, Yu Gu, and L. Ruby Leung
The Cryosphere, 17, 673–697, https://doi.org/10.5194/tc-17-673-2023, https://doi.org/10.5194/tc-17-673-2023, 2023
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We comprehensively evaluated the snow simulations in E3SM land model over the western United States in terms of spatial patterns, temporal correlations, interannual variabilities, elevation gradients, and change with forest cover of snow properties and snow phenology. Our study underscores the need for diagnosing model biases and improving the model representations of snow properties and snow phenology in mountainous areas for more credible simulation and future projection of mountain snowpack.
Timbo Stillinger, Karl Rittger, Mark S. Raleigh, Alex Michell, Robert E. Davis, and Edward H. Bair
The Cryosphere, 17, 567–590, https://doi.org/10.5194/tc-17-567-2023, https://doi.org/10.5194/tc-17-567-2023, 2023
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Understanding global snow cover is critical for comprehending climate change and its impacts on the lives of billions of people. Satellites are the best way to monitor global snow cover, yet snow varies at a finer spatial resolution than most satellite images. We assessed subpixel snow mapping methods across a spectrum of conditions using airborne lidar. Spectral-unmixing methods outperformed older operational methods and are ready to to advance snow cover mapping at the global scale.
Jilu Li, Fernando Rodriguez-Morales, Xavier Fettweis, Oluwanisola Ibikunle, Carl Leuschen, John Paden, Daniel Gomez-Garcia, and Emily Arnold
The Cryosphere, 17, 175–193, https://doi.org/10.5194/tc-17-175-2023, https://doi.org/10.5194/tc-17-175-2023, 2023
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Alaskan glaciers' loss of ice mass contributes significantly to ocean surface rise. It is important to know how deeply and how much snow accumulates on these glaciers to comprehend and analyze the glacial mass loss process. We reported the observed seasonal snow depth distribution from our radar data taken in Alaska in 2018 and 2021, developed a method to estimate the annual snow accumulation rate at Mt. Wrangell caldera, and identified transition zones from wet-snow zones to ablation zones.
Leo-Juhani Meriö, Anssi Rauhala, Pertti Ala-aho, Anton Kuzmin, Pasi Korpelainen, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-242, https://doi.org/10.5194/tc-2022-242, 2023
Revised manuscript accepted for TC
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Information on seasonal snow cover is essential to the understanding of snow processes and operational forecasting. We study the spatiotemporal variability of snow depth and snow processes in subarctic, boreal landscape using drones. We identified multiple theoretically known snow processes and interactions between snow and vegetation. The results highlight the potential of the drones to be used for a detailed study of snow depth in multiple land cover types and snow-vegetation interactions.
Anssi Rauhala, Leo-Juhani Meriö, Anton Kuzmin, Pasi Korpelainen, Pertti Ala-aho, Timo Kumpula, Bjørn Kløve, and Hannu Marttila
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-239, https://doi.org/10.5194/tc-2022-239, 2022
Revised manuscript accepted for TC
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Snow conditions in the northern hemisphere are rapidly changing and information on snow depth is important for decision-making. We present snow depth measurements using different drones throughout the winter in a subarctic site. Generally, all drones produced good estimates of snow depth in open areas. However, differences were observed in the accuracies produced by the different drones and a reduction in accuracy was observed when moving from an open mire area to forest covered areas.
Eunsang Cho, Carrie M. Vuyovich, Sujay V. Kumar, Melissa L. Wrzesien, and Rhae Sung Kim
The Cryosphere Discuss., https://doi.org/10.5194/tc-2022-220, https://doi.org/10.5194/tc-2022-220, 2022
Revised manuscript accepted for TC
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As a future snow mission concept, active microwave sensors have the potential to measure snow water equivalent (SWE) in deep snowpack and forested environments. We used a modeling and data assimilation approach (so-called “Observing System Simulation Experiment”) to quantify the usefulness of active microwave-based SWE retrievals over western Colorado. We found that active microwave sensors with a mature retrieval algorithm can improve SWE simulations by about 20 % in the mountainous domain.
Ghislain Picard, Henning Löwe, and Christian Mätzler
The Cryosphere, 16, 3861–3866, https://doi.org/10.5194/tc-16-3861-2022, https://doi.org/10.5194/tc-16-3861-2022, 2022
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Microwave satellite observations used to monitor the cryosphere require radiative transfer models for their interpretation. These models represent how microwaves are scattered by snow and ice. However no existing theory is suitable for all types of snow and ice found on Earth. We adapted a recently published generic scattering theory to snow and show how it may improve the representation of snows with intermediate densities (~500 kg/m3) and/or with coarse grains at high microwave frequencies.
Leung Tsang, Michael Durand, Chris Derksen, Ana P. Barros, Do-Hyuk Kang, Hans Lievens, Hans-Peter Marshall, Jiyue Zhu, Joel Johnson, Joshua King, Juha Lemmetyinen, Melody Sandells, Nick Rutter, Paul Siqueira, Anne Nolin, Batu Osmanoglu, Carrie Vuyovich, Edward Kim, Drew Taylor, Ioanna Merkouriadi, Ludovic Brucker, Mahdi Navari, Marie Dumont, Richard Kelly, Rhae Sung Kim, Tien-Hao Liao, Firoz Borah, and Xiaolan Xu
The Cryosphere, 16, 3531–3573, https://doi.org/10.5194/tc-16-3531-2022, https://doi.org/10.5194/tc-16-3531-2022, 2022
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Snow water equivalent (SWE) is of fundamental importance to water, energy, and geochemical cycles but is poorly observed globally. Synthetic aperture radar (SAR) measurements at X- and Ku-band can address this gap. This review serves to inform the broad snow research, monitoring, and application communities about the progress made in recent decades to move towards a new satellite mission capable of addressing the needs of the geoscience researchers and users.
Elisabeth D. Hafner, Patrick Barton, Rodrigo Caye Daudt, Jan Dirk Wegner, Konrad Schindler, and Yves Bühler
The Cryosphere, 16, 3517–3530, https://doi.org/10.5194/tc-16-3517-2022, https://doi.org/10.5194/tc-16-3517-2022, 2022
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Knowing where avalanches occur is very important information for several disciplines, for example avalanche warning, hazard zonation and risk management. Satellite imagery can provide such data systematically over large regions. In our work we propose a machine learning model to automate the time-consuming manual mapping. Additionally, we investigate expert agreement for manual avalanche mapping, showing that our network is equally as good as the experts in identifying avalanches.
Joëlle Voglimacci-Stephanopoli, Anna Wendleder, Hugues Lantuit, Alexandre Langlois, Samuel Stettner, Andreas Schmitt, Jean-Pierre Dedieu, Achim Roth, and Alain Royer
The Cryosphere, 16, 2163–2181, https://doi.org/10.5194/tc-16-2163-2022, https://doi.org/10.5194/tc-16-2163-2022, 2022
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Changes in the state of the snowpack in the context of observed global warming must be considered to improve our understanding of the processes within the cryosphere. This study aims to characterize an arctic snowpack using the TerraSAR-X satellite. Using a high-spatial-resolution vegetation classification, we were able to quantify the variability in snow depth, as well as the topographic soil wetness index, which provided a better understanding of the electromagnetic wave–ground interaction.
Jayson Eppler, Bernhard Rabus, and Peter Morse
The Cryosphere, 16, 1497–1521, https://doi.org/10.5194/tc-16-1497-2022, https://doi.org/10.5194/tc-16-1497-2022, 2022
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We introduce a new method for mapping changes in the snow water equivalent (SWE) of dry snow based on differences between time-repeated synthetic aperture radar (SAR) images. It correlates phase differences with variations in the topographic slope which allows the method to work without any "reference" targets within the imaged area and without having to numerically unwrap the spatial phase maps. This overcomes the key challenges faced in using SAR interferometry for SWE change mapping.
Hans Lievens, Isis Brangers, Hans-Peter Marshall, Tobias Jonas, Marc Olefs, and Gabriëlle De Lannoy
The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, https://doi.org/10.5194/tc-16-159-2022, 2022
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Snow depth observations at high spatial resolution from the Sentinel-1 satellite mission are presented over the European Alps. The novel observations can improve our knowledge of seasonal snow mass in areas with complex topography, where satellite-based estimates are currently lacking, and benefit a number of applications including water resource management, flood forecasting, and numerical weather prediction.
Julien Meloche, Alexandre Langlois, Nick Rutter, Alain Royer, Josh King, Branden Walker, Philip Marsh, and Evan J. Wilcox
The Cryosphere, 16, 87–101, https://doi.org/10.5194/tc-16-87-2022, https://doi.org/10.5194/tc-16-87-2022, 2022
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To estimate snow water equivalent from space, model predictions of the satellite measurement (brightness temperature in our case) have to be used. These models allow us to estimate snow properties from the brightness temperature by inverting the model. To improve SWE estimate, we proposed incorporating the variability of snow in these model as it has not been taken into account yet. A new parameter (coefficient of variation) is proposed because it improved simulation of brightness temperature.
Christopher Donahue, S. McKenzie Skiles, and Kevin Hammonds
The Cryosphere, 16, 43–59, https://doi.org/10.5194/tc-16-43-2022, https://doi.org/10.5194/tc-16-43-2022, 2022
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The amount of water within a snowpack is important information for predicting snowmelt and wet-snow avalanches. From within a controlled laboratory, the optimal method for measuring liquid water content (LWC) at the snow surface or along a snow pit profile using near-infrared imagery was determined. As snow samples melted, multiple models to represent wet-snow reflectance were assessed against a more established LWC instrument. The best model represents snow as separate spheres of ice and water.
Zacharie Barrou Dumont, Simon Gascoin, Olivier Hagolle, Michaël Ablain, Rémi Jugier, Germain Salgues, Florence Marti, Aurore Dupuis, Marie Dumont, and Samuel Morin
The Cryosphere, 15, 4975–4980, https://doi.org/10.5194/tc-15-4975-2021, https://doi.org/10.5194/tc-15-4975-2021, 2021
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Since 2020, the Copernicus High Resolution Snow & Ice Monitoring Service has distributed snow cover maps at 20 m resolution over Europe in near-real time. These products are derived from the Sentinel-2 Earth observation mission, with a revisit time of 5 d or less (cloud-permitting). Here we show the good accuracy of the snow detection over a wide range of regions in Europe, except in dense forest regions where the snow cover is hidden by the trees.
Xiaodan Wu, Kathrin Naegeli, Valentina Premier, Carlo Marin, Dujuan Ma, Jingping Wang, and Stefan Wunderle
The Cryosphere, 15, 4261–4279, https://doi.org/10.5194/tc-15-4261-2021, https://doi.org/10.5194/tc-15-4261-2021, 2021
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We performed a comprehensive accuracy assessment of an Advanced Very High Resolution Radiometer global area coverage snow-cover extent time series dataset for the Hindu Kush Himalayan (HKH) region. The sensor-to-sensor consistency, the accuracy related to snow depth, elevations, land-cover types, slope, and aspects, and topographical variability were also explored. Our analysis shows an overall accuracy of 94 % in comparison with in situ station data, which is the same with MOD10A1 V006.
Pia Nielsen-Englyst, Jacob L. Høyer, Kristine S. Madsen, Rasmus T. Tonboe, Gorm Dybkjær, and Sotirios Skarpalezos
The Cryosphere, 15, 3035–3057, https://doi.org/10.5194/tc-15-3035-2021, https://doi.org/10.5194/tc-15-3035-2021, 2021
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The Arctic region is responding heavily to climate change, and yet, the air temperature of Arctic ice-covered areas is heavily under-sampled when it comes to in situ measurements. This paper presents a method for estimating daily mean 2 m air temperatures (T2m) in the Arctic from satellite observations of skin temperature, providing spatially detailed observations of the Arctic. The satellite-derived T2m product covers clear-sky snow and ice surfaces in the Arctic for the period 2000–2009.
Pinja Venäläinen, Kari Luojus, Juha Lemmetyinen, Jouni Pulliainen, Mikko Moisander, and Matias Takala
The Cryosphere, 15, 2969–2981, https://doi.org/10.5194/tc-15-2969-2021, https://doi.org/10.5194/tc-15-2969-2021, 2021
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Information about snow water equivalent (SWE) is needed in many applications, including climate model evaluation and forecasting fresh water availability. Space-borne radiometer observations combined with ground snow depth measurements can be used to make global estimates of SWE. In this study, we investigate the possibility of using sparse snow density measurement in satellite-based SWE retrieval and show that using the snow density information in post-processing improves SWE estimations.
Linlu Mei, Vladimir Rozanov, Christine Pohl, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2757–2780, https://doi.org/10.5194/tc-15-2757-2021, https://doi.org/10.5194/tc-15-2757-2021, 2021
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This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 1 of two companion papers and shows the method description and sensitivity study. The paper investigates the major factors, including the assumptions of snow optical properties, snow particle distribution and atmospheric conditions (cloud and aerosol), impacting snow property retrievals from satellite observation.
Linlu Mei, Vladimir Rozanov, Evelyn Jäkel, Xiao Cheng, Marco Vountas, and John P. Burrows
The Cryosphere, 15, 2781–2802, https://doi.org/10.5194/tc-15-2781-2021, https://doi.org/10.5194/tc-15-2781-2021, 2021
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This paper presents a new snow property retrieval algorithm from satellite observations. This is Part 2 of two companion papers and shows the results and validation. The paper performs the new retrieval algorithm on the Sea and Land
Surface Temperature Radiometer (SLSTR) instrument and compares the retrieved snow properties with ground-based measurements, aircraft measurements and other satellite products.
Ahmad Hojatimalekshah, Zachary Uhlmann, Nancy F. Glenn, Christopher A. Hiemstra, Christopher J. Tennant, Jake D. Graham, Lucas Spaete, Arthur Gelvin, Hans-Peter Marshall, James P. McNamara, and Josh Enterkine
The Cryosphere, 15, 2187–2209, https://doi.org/10.5194/tc-15-2187-2021, https://doi.org/10.5194/tc-15-2187-2021, 2021
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We describe the relationships between snow depth, vegetation canopy, and local-scale processes during the snow accumulation period using terrestrial laser scanning (TLS). In addition to topography and wind, our findings suggest the importance of fine-scale tree structure, species type, and distributions on snow depth. Snow depth increases from the canopy edge toward the open areas, but wind and topographic controls may affect this trend. TLS data are complementary to wide-area lidar surveys.
Jennifer M. Jacobs, Adam G. Hunsaker, Franklin B. Sullivan, Michael Palace, Elizabeth A. Burakowski, Christina Herrick, and Eunsang Cho
The Cryosphere, 15, 1485–1500, https://doi.org/10.5194/tc-15-1485-2021, https://doi.org/10.5194/tc-15-1485-2021, 2021
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This pilot study describes a proof of concept for using lidar on an unpiloted aerial vehicle to map shallow snowpack (< 20 cm) depth in open terrain and forests. The 1 m2 resolution snow depth map, generated by subtracting snow-off from snow-on lidar-derived digital terrain models, consistently had 0.5 to 1 cm precision in the field, with a considerable reduction in accuracy in the forest. Performance depends on the point cloud density and the ground surface variability and vegetation.
Elisabeth D. Hafner, Frank Techel, Silvan Leinss, and Yves Bühler
The Cryosphere, 15, 983–1004, https://doi.org/10.5194/tc-15-983-2021, https://doi.org/10.5194/tc-15-983-2021, 2021
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Satellites prove to be very valuable for documentation of large-scale avalanche periods. To test reliability and completeness, which has not been satisfactorily verified before, we attempt a full validation of avalanches mapped from two optical sensors and one radar sensor. Our results demonstrate the reliability of high-spatial-resolution optical data for avalanche mapping, the suitability of radar for mapping of larger avalanches and the unsuitability of medium-spatial-resolution optical data.
Xiongxin Xiao, Shunlin Liang, Tao He, Daiqiang Wu, Congyuan Pei, and Jianya Gong
The Cryosphere, 15, 835–861, https://doi.org/10.5194/tc-15-835-2021, https://doi.org/10.5194/tc-15-835-2021, 2021
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Daily time series and full space-covered sub-pixel snow cover area data are urgently needed for climate and reanalysis studies. Due to the fact that observations from optical satellite sensors are affected by clouds, this study attempts to capture dynamic characteristics of snow cover at a fine spatiotemporal resolution (daily; 6.25 km) accurately by using passive microwave data. We demonstrate the potential to use the passive microwave and the MODIS data to map the fractional snow cover area.
Marco Bongio, Ali Nadir Arslan, Cemal Melih Tanis, and Carlo De Michele
The Cryosphere, 15, 369–387, https://doi.org/10.5194/tc-15-369-2021, https://doi.org/10.5194/tc-15-369-2021, 2021
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The capability of time-lapse photography to retrieve snow depth time series was tested. We demonstrated that this method can be efficiently used in three different case studies: two in the Italian Alps and one in a forested region of Finland, with an accuracy comparable to the most common methods such as ultrasonic sensors or manual measurements. We hope that this simple method based only on a camera and a graduated stake can enable snow depth measurements in dangerous and inaccessible sites.
Lucie A. Eberhard, Pascal Sirguey, Aubrey Miller, Mauro Marty, Konrad Schindler, Andreas Stoffel, and Yves Bühler
The Cryosphere, 15, 69–94, https://doi.org/10.5194/tc-15-69-2021, https://doi.org/10.5194/tc-15-69-2021, 2021
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In spring 2018 in the alpine Dischma valley (Switzerland), we tested different industrial photogrammetric platforms for snow depth mapping. These platforms were high-resolution satellites, an airplane, unmanned aerial systems and a terrestrial system. Therefore, this study gives a general overview of the accuracy and precision of the different photogrammetric platforms available in space and on earth and their use for snow depth mapping.
Maxim Lamare, Marie Dumont, Ghislain Picard, Fanny Larue, François Tuzet, Clément Delcourt, and Laurent Arnaud
The Cryosphere, 14, 3995–4020, https://doi.org/10.5194/tc-14-3995-2020, https://doi.org/10.5194/tc-14-3995-2020, 2020
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Terrain features found in mountainous regions introduce large errors into the calculation of the physical properties of snow using optical satellite images. We present a new model performing rapid calculations of solar radiation over snow-covered rugged terrain that we tested over a site in the French Alps. The results of the study show that all the interactions between sunlight and the terrain should be accounted for over snow-covered surfaces to correctly estimate snow properties from space.
Tim Carlsen, Gerit Birnbaum, André Ehrlich, Veit Helm, Evelyn Jäkel, Michael Schäfer, and Manfred Wendisch
The Cryosphere, 14, 3959–3978, https://doi.org/10.5194/tc-14-3959-2020, https://doi.org/10.5194/tc-14-3959-2020, 2020
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The angular reflection of solar radiation by snow surfaces is particularly anisotropic and highly variable. We measured the angular reflection from an aircraft using a digital camera in Antarctica in 2013/14 and studied its variability: the anisotropy increases with a lower Sun but decreases for rougher surfaces and larger snow grains. The applied methodology allows for a direct comparison with satellite observations, which generally underestimated the anisotropy measured within this study.
César Deschamps-Berger, Simon Gascoin, Etienne Berthier, Jeffrey Deems, Ethan Gutmann, Amaury Dehecq, David Shean, and Marie Dumont
The Cryosphere, 14, 2925–2940, https://doi.org/10.5194/tc-14-2925-2020, https://doi.org/10.5194/tc-14-2925-2020, 2020
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We evaluate a recent method to map snow depth based on satellite photogrammetry. We compare it with accurate airborne laser-scanning measurements in the Sierra Nevada, USA. We find that satellite data capture the relationship between snow depth and elevation at the catchment scale and also small-scale features like snow drifts and avalanche deposits. We conclude that satellite photogrammetry stands out as a convenient method to estimate the spatial distribution of snow depth in high mountains.
Phillip Harder, John W. Pomeroy, and Warren D. Helgason
The Cryosphere, 14, 1919–1935, https://doi.org/10.5194/tc-14-1919-2020, https://doi.org/10.5194/tc-14-1919-2020, 2020
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Unmanned-aerial-vehicle-based (UAV) structure-from-motion (SfM) techniques have the ability to map snow depths in open areas. Here UAV lidar and SfM are compared to map sub-canopy snowpacks. Snow depth accuracy was assessed with data from sites in western Canada collected in 2019. It is demonstrated that UAV lidar can measure the sub-canopy snow depth at a high accuracy, while UAV-SfM cannot. UAV lidar promises to quantify snow–vegetation interactions at unprecedented accuracy and resolution.
Carlo Marin, Giacomo Bertoldi, Valentina Premier, Mattia Callegari, Christian Brida, Kerstin Hürkamp, Jochen Tschiersch, Marc Zebisch, and Claudia Notarnicola
The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, https://doi.org/10.5194/tc-14-935-2020, 2020
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In this paper, we use for the first time the synthetic aperture radar (SAR) time series acquired by Sentinel-1 to monitor snowmelt dynamics in alpine regions. We found that the multitemporal SAR allows the identification of the three phases that characterize the melting process, i.e., moistening, ripening and runoff, in a spatial distributed way. We believe that the presented investigation could have relevant applications for monitoring and predicting the snowmelt progress over large regions.
Edward H. Bair, Karl Rittger, Jawairia A. Ahmad, and Doug Chabot
The Cryosphere, 14, 331–347, https://doi.org/10.5194/tc-14-331-2020, https://doi.org/10.5194/tc-14-331-2020, 2020
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Ice and snowmelt feed the Indus River and Amu Darya, but validation of estimates from satellite sensors has been a problem until recently, when we were given daily snow depth measurements from these basins. Using these measurements, estimates of snow on the ground were created and compared with models. Estimates of water equivalent in the snowpack were mostly in agreement. Stratigraphy was also modeled and showed 1 year with a relatively stable snowpack but another with multiple weak layers.
Nick Rutter, Melody J. Sandells, Chris Derksen, Joshua King, Peter Toose, Leanne Wake, Tom Watts, Richard Essery, Alexandre Roy, Alain Royer, Philip Marsh, Chris Larsen, and Matthew Sturm
The Cryosphere, 13, 3045–3059, https://doi.org/10.5194/tc-13-3045-2019, https://doi.org/10.5194/tc-13-3045-2019, 2019
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Impact of natural variability in Arctic tundra snow microstructural characteristics on the capacity to estimate snow water equivalent (SWE) from Ku-band radar was assessed. Median values of metrics quantifying snow microstructure adequately characterise differences between snowpack layers. Optimal estimates of SWE required microstructural values slightly less than the measured median but tolerated natural variability for accurate estimation of SWE in shallow snowpacks.
Alexandre R. Bevington, Hunter E. Gleason, Vanessa N. Foord, William C. Floyd, and Hardy P. Griesbauer
The Cryosphere, 13, 2693–2712, https://doi.org/10.5194/tc-13-2693-2019, https://doi.org/10.5194/tc-13-2693-2019, 2019
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We investigate the influence of ocean–atmosphere teleconnections on the start, end, and duration of snow cover in British Columbia, Canada. We do this using daily satellite imagery from 2002 to 2018 and assess the accuracy of our methods using reported snow cover at 60 weather stations. We found that there are very strong relationships that vary by region and elevation. This improves our understanding of snow cover distribution and could be used to predict snow cover from ocean–climate indices.
Anne Braakmann-Folgmann and Craig Donlon
The Cryosphere, 13, 2421–2438, https://doi.org/10.5194/tc-13-2421-2019, https://doi.org/10.5194/tc-13-2421-2019, 2019
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Snow on sea ice is a fundamental climate variable. We propose a novel approach to estimate snow depth on sea ice from satellite microwave radiometer measurements at several frequencies using neural networks (NNs). We evaluate our results with airborne snow depth measurements and compare them to three other established snow depth algorithms. We show that our NN results agree better with the airborne data than the other algorithms. This is also advantageous for sea ice thickness calculation.
Jie Deng, Tao Che, Cunde Xiao, Shijin Wang, Liyun Dai, and Akynbekkyzy Meerzhan
The Cryosphere, 13, 2149–2167, https://doi.org/10.5194/tc-13-2149-2019, https://doi.org/10.5194/tc-13-2149-2019, 2019
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The Chinese ski industry is rapidly booming driven by enormous market demand and government support with the coming 2022 Beijing Winter Olympics. We evaluate the locational suitability of ski areas in China by integrating the natural and socioeconomic conditions. Corresponding development strategies for decision-makers are proposed based on the multi-criteria metrics, which will be extended to incorporate potential influences from future climate change and socioeconomic development.
Isobel R. Lawrence, Michel C. Tsamados, Julienne C. Stroeve, Thomas W. K. Armitage, and Andy L. Ridout
The Cryosphere, 12, 3551–3564, https://doi.org/10.5194/tc-12-3551-2018, https://doi.org/10.5194/tc-12-3551-2018, 2018
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In this paper we estimate the thickness of snow cover on Arctic sea ice from space. We use data from two radar altimeter satellites, AltiKa and CryoSat-2, that have been operating synchronously since 2013. We produce maps of monthly average snow depth for the four growth seasons (October to April): 2012–2013, 2013–2014, 2014–2015, and 2015–2016. Snow depth estimates are essential for the accurate retrieval of sea ice thickness from satellite altimetry.
Richard Fernandes, Christian Prevost, Francis Canisius, Sylvain G. Leblanc, Matt Maloley, Sarah Oakes, Kiyomi Holman, and Anders Knudby
The Cryosphere, 12, 3535–3550, https://doi.org/10.5194/tc-12-3535-2018, https://doi.org/10.5194/tc-12-3535-2018, 2018
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The use of lightweight UAV-based surveys of surface elevation to map snow depth and weekly snow depth change was evaluated over five study areas spanning a range of topography and vegetation cover. Snow depth was estimated with an accuracy of better than 10 cm in the vertical and 3 cm in the horizontal. Vegetation in the snow-free elevation map was a major source of error. As a result, the snow depth change between two dates with snow cover was estimated with an accuracy of better than 4 cm.
Todd A. N. Redpath, Pascal Sirguey, and Nicolas J. Cullen
The Cryosphere, 12, 3477–3497, https://doi.org/10.5194/tc-12-3477-2018, https://doi.org/10.5194/tc-12-3477-2018, 2018
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A remotely piloted aircraft system (RPAS) is evaluated for mapping seasonal snow depth across an alpine basin. RPAS photogrammetry performs well at providing maps of snow depth at high spatial resolution, outperforming field measurements for resolving spatial variability. Uncertainty and error analysis reveal limitations and potential pitfalls of photogrammetric surface-change analysis. Ultimately, RPAS can be a useful tool for understanding snow processes and improving snow modelling efforts.
Alexander Kokhanovsky, Maxim Lamare, Biagio Di Mauro, Ghislain Picard, Laurent Arnaud, Marie Dumont, François Tuzet, Carsten Brockmann, and Jason E. Box
The Cryosphere, 12, 2371–2382, https://doi.org/10.5194/tc-12-2371-2018, https://doi.org/10.5194/tc-12-2371-2018, 2018
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This work presents a new technique with which to derive the snow microphysical and optical properties from snow spectral reflectance measurements. The technique is robust and easy to use, and it does not require the extraction of snow samples from a given snowpack. It can be used in processing satellite imagery over extended fresh dry, wet and polluted snowfields.
Stefan Härer, Matthias Bernhardt, Matthias Siebers, and Karsten Schulz
The Cryosphere, 12, 1629–1642, https://doi.org/10.5194/tc-12-1629-2018, https://doi.org/10.5194/tc-12-1629-2018, 2018
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The paper presents an approach which can be used to process satellite-based snow cover maps with a higher-than-today accuracy at the local scale. Many of the current satellite-based snow maps are using the NDSI with a threshold as a tool for deciding if there is snow on the ground or not. The presented study has shown that, firstly, using the standard threshold of 0.4 can result in significant derivations at the local scale and that, secondly, the deviations become smaller for coarser scales.
Cited articles
Abermann, J., Eckerstorfer, M., Malnes, E., and Hansen, B. U.: A large wet snow
avalanche cycle in West Greenland quantified using remote sensing and in situ
observations, Nat. Hazards, 97, 517–534, 2019. a
Abermann, J., Langley, K., Myreng, S. M., Rasmussen, K., and Petersen, D.: Heterogeneous timing of freshwater input into Kobbefjord, a low-arctic fjord in Greenland, Hydrol. Process., 35, e14413, https://doi.org/10.1002/hyp.14413, 2021. a, b
Arslan, A., Tanis, C., Metsämäki, S., Aurela, M., Böttcher, K., Linkosalmi,
M., and Peltoniemi, M.: Automated webcam monitoring of fractional snow cover
in northern boreal conditions, Geosciences, 7, 55, https://doi.org/10.3390/geosciences7030055, 2017. a
Assmann, J. J., Myers-Smith, I. H., Phillimore, A. B., Bjorkman, A. D., Ennos,
R. E., Prevéy, J. S., Henry, G. H., Schmidt, N. M., and Hollister, R. D.:
Local snow melt and temperature – but not regional sea ice–explain
variation in spring phenology in coastal Arctic tundra, Glob. Change
Biol., 25, 2258–2274, https://doi.org/10.1111/gcb.14639, 2019. a, b
Bair, E. H., Rittger, K., Davis, R. E., Painter, T. H., and Dozier, J.:
Validating reconstruction of snow water equivalent in California's Sierra
Nevada using measurements from the NASA Airborne Snow Observatory, Water
Resour. Res., 52, 8437–8460,
https://doi.org/10.1002/2016WR018704, 2016. a
Bay, C., Aastrup, P., and Nymand, J.: The NERO line, A vegetation transect in
Kobbefjord, West Greenland, technical report, National Enviromental Research Institute, Aarhus University, Denmark, 40 pp., ISBN 978-87-7073-070-9, available at: https://www.dmu.dk/Pub/FR693.pdf (last access: 25 July 2021), 2008. a
Box, J. E., Colgan, W. T., Christensen, T. R., Schmidt, N. M., Lund, M.,
Parmentier, F.-J. W., Brown, R., Bhatt, U. S., Euskirchen, E. S., Romanovsky,
V. E., Walsh, J. E., Overland, J. E., Wang, M., Corell, R. W., Meier, W. N.,
Wouters, B., Mernild, S., Mård, J., Pawlak, J., and Olsen,
M. S.: Key indicators of Arctic climate change: 1971–2017, Environ.
Res. Lett., 14, 045010, https://doi.org/10.1088/1748-9326/aafc1b, 2019. a, b
Brown, R. D. and Robinson, D. A.: Northern Hemisphere spring snow cover variability and change over 1922–2010 including an assessment of uncertainty, The Cryosphere, 5, 219–229, https://doi.org/10.5194/tc-5-219-2011, 2011. a
Buchelt, S.: georef_webcam V.1.0 (v1.0), Zenodo [code], https://doi.org/10.5281/zenodo.3899960, 2020. a
Candela, S. G., Howat, I., Noh, M.-J., Porter, C. C., and Morin, P. J.:
ArcticDEM Validation and Accuracy Assessment, in: AGU Fall
Meeting Abstracts, New Orleans, Louisiana, US, 11–15 December 2017, available at: https://agu.confex.com/agu/fm17/meetingapp.cgi/Paper/240260 (last access: 12 August 2021), 2017. a
Corripio, J. G.: Snow surface albedo estimation using terrestrial photography,
International J. Remote Sens., 25, 5705–5729,
https://doi.org/10.1080/01431160410001709002, 2004. a
Dietz, A. J., Kuenzer, C., Gessner, U., and Dech, S.: Remote sensing of snow
– a review of available methods, Int. J. Remote Sens.,
33, 4094–4134, https://doi.org/10.1080/01431161.2011.640964, 2012. a, b, c
Dong, C.: Remote sensing, hydrological modeling and in situ observations in
snow cover research: A review, J. Hydrol., 561, 573–583,
https://doi.org/10.1016/j.jhydrol.2018.04.027, 2018. a
Elberling, B., Tamstorf, M. P., Michelsen, A., Arndal, M. F., Sigsgaard, C.,
Illeris, L., Bay, C., Hansen, B. U., Christensen, T. R., Hansen, E. S.,
Jakobsen, B. H., and Beyens, L.: Soil and Plant
Community – Characteristics and Dynamics at Zackenberg, in:
High-Arctic Ecosystem Dynamics in a Changing Climate,
Adv. Ecol. Res., 40, pp. 223–248, https://doi.org/10.1016/S0065-2504(07)00010-4, 2008. a, b, c, d
ESA: Sentinel-1: Sentinel-1: ESA’s Radar Observatory Mission for GMES Operational Services, Tech. Rep., edited by: Fletcher, K., ESA Communications,
ESA SP-1322/1, ISBN 978-92-9221-418-0, available at: https://earth.esa.int/eogateway/documents/20142/0/Sentinel-1-ESA's-Radar-Observatory-Mission-for-GMES-Operational-Services.pdf (last access: 6 June 2021), 2012. a
ESA: Observation Scenario Archive, European Space Agency, available at:
https://sentinel.esa.int/web/sentinel/missions/sentinel-1/observation-scenario/archive, last access: 8 February 2020. a
Farinotti, D., Magnusson, J., Huss, M., and Bauder, A.: Snow accumulation
distribution inferred from time-lapse photography and simple modelling,
Hydrol. Process., 24, 2087–2097, https://doi.org/10.1002/hyp.7629, 2010. a, b
Gascoin, S., Grizonnet, M., Bouchet, M., Salgues, G., and Hagolle, O.: Theia Snow collection: high-resolution operational snow cover maps from Sentinel-2 and Landsat-8 data, Earth Syst. Sci. Data, 11, 493–514, https://doi.org/10.5194/essd-11-493-2019, 2019. a
GCOS-WMO: Essential Climate Variables, Global Climate Observing System – World Meteorological
Organization, available at:
https://gcos.wmo.int/en/essential-climate-variables, last access: 9 March 2020. a
Girona-Mata, M., Miles, E. S., Ragettli, S., and Pellicciotti, F.:
High-resolution snowline delineation from Landsat imagery to infer snow
cover controls in a Himalayan catchment, Water Resour. Res., 55,
6754–6772, 2019. a
Hansen, B. U., Sigsgaard, C., Rasmussen, L., Cappelen, J., Hinkler, J.,
Mernild, S. H., Petersen, D., Tamstorf, M. P., Rasch, M., and Hasholt, B.:
Present-Day Climate at Zackenberg, in: High-Arctic Ecosystem
Dynamics in a Changing Climate, Adv.
Ecol. Res., 40, 111–149, https://doi.org/10.1016/S0065-2504(07)00006-2, 2008. a, b
Hinkler, J., Hansen, B. U., Tamstorf, M. P., Sigsgaard, C., and Petersen, D.:
Snow and Snow-Cover in Central Northeast Greenland, in:
High-Arctic Ecosystem Dynamics in a Changing Climate, Adv. Ecol. Res., 40, 175–195, https://doi.org/10.1016/S0065-2504(07)00008-6, 2008. a
Hock, R., Rasul, G., Adler, C., Cáceres, B., Gruber, S., Hirabayashi, Y.,
Jackson, M., Kääb, A., Kang, S., Kutuzov, S., Milner, A., Molau, U., Morin,
S., Orlove, B., and Steltzer, H.: High Mountain Areas, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., The
Intergovernmental Panel on Climate Change (IPCC), Switzerland, 131–202, available at: https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/06_SROCC_Ch02_FINAL.pdf (last access: 12 August 2021),
2019. a, b
Härer, S., Bernhardt, M., and Schulz, K.: PRACTISE – Photo Rectification And ClassificaTIon SoftwarE (V.2.1), Geosci. Model Dev., 9, 307–321, https://doi.org/10.5194/gmd-9-307-2016, 2016. a
Ide, R. and Oguma, H.: A cost-effective monitoring method using digital
time-lapse cameras for detecting temporal and spatial variations of snowmelt
and vegetation phenology in alpine ecosystems, Ecol. Inform., 16, 25–34, https://doi.org/10.1016/j.ecoinf.2013.04.003, 2013. a, b, c
Kankaanpää, T., Skov, K., Abrego, N., Lund, M., Schmidt, N. M., and Roslin,
T.: Spatiotemporal snowmelt patterns within a high Arctic landscape, with
implications for flora and fauna, Arct. Antarct. Alp. Res., 50,
e1415624, https://doi.org/10.1080/15230430.2017.1415624, 2018. a, b
Kepski, D., Luks, B., Migała, K., Wawrzyniak, T., Westermann, S., and Wojtuń,
B.: Terrestrial Remote Sensing of Snowmelt in a Diverse
High-Arctic Tundra Environment Using Time-Lapse Imagery,
Remote Sensing, 9, 733, https://doi.org/10.3390/rs9070733, 2017. a, b, c
Kerr, T., Clark, M., Hendrikx, J., and Anderson, B.: Snow distribution in a
steep mid-latitude alpine catchment, Adv. Water Resour., 55, 17–24, https://doi.org/10.1016/j.advwatres.2012.12.010, 2013. a
Koskinen, J. T., Pulliainen, J. T., Luojus, K. P., and Takala, M.: Monitoring
of snow-cover properties during the spring melting period in forested areas, IEEE T. Geosci. Remote, 48, 50–58, 2009. a
Lehning, M., Löwe, H., Ryser, M., and Raderschall, N.: Inhomogeneous
precipitation distribution and snow transport in steep terrain, Water
Resour. Res., 44, W07404, https://doi.org/10.1029/2007WR006545, 2008. a
Lievens, H., Demuzere, M., Marshall, H.-P., Reichle, R. H., Brucker, L.,
Brangers, I., de Rosnay, P., Dumont, M., Girotto, M., Immerzeel, W. W., and
others: Snow depth variability in the Northern Hemisphere mountains
observed from space, Nat. Commun., 10, 4629, https://doi.org/10.1038/s41467-019-12566-y, 2019. a, b, c, d
Lievens, H., Brangers, I., Marshall, H.-P., Jonas, T., Olefs, M., and De Lannoy, G.: Sentinel-1 snow depth retrieval at sub-kilometer resolution over the European Alps, The Cryosphere, 16, 159–177, https://doi.org/10.5194/tc-16-159-2022, 2022. a
Luojus, K. P., Pulliainen, J. T., Metsamaki, S., and Hallikainen, M. T.:
Accuracy assessment of SAR data-based snow-covered area estimation method,
IEEE T. Geosci. Remote, 44, 277–287, 2006. a
López-Blanco, E., Jackowicz-Korczynski, M., Mastepanov, M., Skov, K.,
Westergaard-Nielsen, A., Williams, M., and Christensen, T. R.: Multi-year
data-model evaluation reveals the importance of nutrient availability over
climate in arctic ecosystem C dynamics, Environ. Res. Lett., 15,
094007, https://doi.org/10.1088/1748-9326/ab865b, 2020. a, b, c
Marin, C., Bertoldi, G., Premier, V., Callegari, M., Brida, C., Hürkamp, K., Tschiersch, J., Zebisch, M., and Notarnicola, C.: Use of Sentinel-1 radar observations to evaluate snowmelt dynamics in alpine regions, The Cryosphere, 14, 935–956, https://doi.org/10.5194/tc-14-935-2020, 2020. a, b, c, d, e, f, g, h, i, j, k, l, m, n, o, p, q, r, s, t, u, v
Meddens, A. J., Vierling, L. A., Eitel, J. U., Boelman, N. T., Jennewein, J.,
and Maguire, A.: Characterizing and evaluating the Arctic Digital
Elevation Model product with LiDAR data for spatial modeling, in: 2017
ABoVE Science Team Meeting, Colorado, US, 17–20 January 2017, available at: https://above.nasa.gov/meeting_jan2017/docs/posters/Meddens_eitel_boelman_astm3_poster.pdf (last access: 12 August 2021),
2017. a
Meltofte, H. and Rasch, M.: The Study Area at Zackenberg, in:
High-Arctic Ecosystem Dynamics in a Changing Climate, Adv. Ecol. Res., 40, 101–110, https://doi.org/10.1016/S0065-2504(07)00005-0, 2008. a
Meredith, M., Sommerkorn, M., Cassota, S., Derksen, C., Ekaykin, A., Hollowed,
A., Kofinas, G., Mackintosh, A., Melbourne-Thomas, J., Muelbert, M. M. C.,
Ottersen, G., Pritchard, H., and Schuur, E. A. G.: Polar Regions, in: IPCC Special Report on the Ocean and Cryosphere in a Changing Climate, edited by: Pörtner, H.-O., Roberts, D. C., Masson-Delmotte, V., Zhai, P., Tignor, M., Poloczanska, E., Mintenbeck, K., Alegría, A., Nicolai, M., Okem, A., Petzold, J., Rama, B., and Weyer, N. M., Intergovernmental Panel on Climate Change (IPCC), Switzerland, 203–320, available at: https://www.ipcc.ch/site/assets/uploads/sites/3/2019/11/07_SROCC_Ch03_FINAL.pdf (last access: 12 August 2021),
2019. a, b
Mernild, S. H., Liston, G. E., and Hasholt, B.: Snow-distribution and melt
modelling for glaciers in Zackenberg river drainage basin, north-eastern
Greenland, Hydrol. Process., 21,
3249–3263, 2007. a
Molotch, N. P. and Margulis, S. A.: Estimating the distribution of snow water
equivalent using remotely sensed snow cover data and a spatially distributed
snowmelt model: A multi-resolution, multi-sensor comparison, Adv.
Water Resour., 31, 1503–1514,
https://doi.org/10.1016/j.advwatres.2008.07.017, 2008. a
Mott, R., Egli, L., Grünewald, T., Dawes, N., Manes, C., Bavay, M., and Lehning, M.: Micrometeorological processes driving snow ablation in an Alpine catchment, The Cryosphere, 5, 1083–1098, https://doi.org/10.5194/tc-5-1083-2011, 2011. a
Mott, R., Gromke, C., Grünewald, T., and Lehning, M.: Relative importance of
advective heat transport and boundary layer decoupling in the melt dynamics
of a patchy snow cover, Adv. Water Resour., 55, 88–97,
https://doi.org/10.1016/j.advwatres.2012.03.001, 2013. a
Mott, R., Vionnet, V., and Grünewald, T.: The seasonal snow cover dynamics:
review on wind-driven coupling processes, Front. Earth Sci., 6, 197, https://doi.org/10.3389/feart.2018.00197, 2018. a, b
Myreng, S. M., Rasmussen, K. K., and Hass, A. E.: NuukBasic Snow Survey 2020, Data
collection, post processing and results, technical report, Asiaq-Greenland Survey, Nuuk, Greenland, 31 pp.,
2020. a
Nagler, T., Rott, H., Ossowska, J., Schwaizer, G., Small, D., Malnes, E., Luojus, K., Metsämäki, S., and Pinnock, S.: Snow Cover Monitoring by Synergistic Use of Sentinel-
3 Slstr and Sentinel-L Sar Data, in: IGARSS 2018–2018 IEEE International Geoscience and Remote Sensing Symposium, Valencia, Spain, 22–17 July 2018, Int. Geosci. Remote Se., 8727–8730, https://doi.org/10.1109/IGARSS.2018.8518203, 2018. a, b
Niittynen, P., Heikkinen, R. K., and Luoto, M.: Snow cover is a neglected
driver of Arctic biodiversity loss, Nat. Clim. Change, 8, 997–1001, 2018. a
Noh, M.-J. and Howat, I. M.: Automated stereo-photogrammetric DEM generation
at high latitudes: Surface Extraction with TIN-based Search-space
Minimization (SETSM) validation and demonstration over glaciated regions,
GISci. Remote Sens., 52, 198–217,
https://doi.org/10.1080/15481603.2015.1008621, 2015. a
Pedersen, S. H., Tamstorf, M. P., Abermann, J., Westergaard-Nielsen, A., Lund,
M., Skov, K., Sigsgaard, C., Mylius, M. R., Hansen, B. U., Liston, G. E., and
Schmidt, N. M.: Spatiotemporal Characteristics of Seasonal Snow Cover
in Northeast Greenland from in Situ Observations, Arct. Antarct.
Alp. Res., 48, 653–671, https://doi.org/10.1657/AAAR0016-028,
2016. a, b, c, d
Pedersen, S. H., Liston, G. E., Tamstorf, M. P., Abermann, J., Lund, M., and
Schmidt, N. M.: Quantifying snow controls on vegetation greenness, Ecosphere,
9, e02309, https://doi.org/10.1002/ecs2.2309, 2018. a, b, c, d
Piazzi, G., Tanis, C. M., Kuter, S., Simsek, B., Puca, S., Toniazzo, A.,
Takala, M., Akyürek, Z., Gabellani, S., and Arslan, A. N.: Cross-Country
Assessment of H-SAF Snow Products by Sentinel-2 Imagery
Validated against In-Situ Observations and Webcam Photography,
Geosciences, 9, 129, https://doi.org/10.3390/geosciences9030129, 2019. a
Planet Team: Planet Application Program Interface: In Space for
Life on Earth, Planet [data set], available at: http://www.planet.com,
last access: 12 June 2021. a
Polar Geospatial Center: Public HTTP Data Repository, Polar Geospatial Center [data set], available at:
http://data.pgc.umn.edu/elev/dem/setsm/ArcticDEM/, last
access: 8 June 2021. a
Portenier, C., Hüsler, F., Härer, S., and Wunderle, S.: Towards a webcam-based snow cover monitoring network: methodology and evaluation, The Cryosphere, 14, 1409–1423, https://doi.org/10.5194/tc-14-1409-2020, 2020. a, b
Porter, C., Morin, P., Howat, I., Noh, M.-J., Bates, B., Peterman, K., Keesey,
S., Schlenk, M., Gardiner, J., Tomko, K., Willis, M., Kelleher, C., Cloutier,
M., Husby, E., Foga, S., Nakamura, H., Platson, M., Wethington, Michael, J.,
Williamson, C., Bauer, G., Enos, J., Arnold, G., Kramer, W., Becker, P.,
Doshi, A., D'Souza, C., Cummens, P., Laurier, F., and Bojesen, M.:
ArcticDEM, V1, Havard Dataverse [data set], https://doi.org/10.7910/DVN/OHHUKH, 2018. a, b, c, d
Rasmussen, K. K., Christensen, L. H., Iversen, K. M., Hill, K., Rasch, M., Rudd, D., Burup, M., Westh, S. M., Pedersen, S. H., Christensen, T. R., Lund, M.,
Mastepanov, M., Tamstorf, M. P., Westergaard-Nielsen, A., Madsen, M.,
Sigsgaard, C., Pedersen, M. R., Ström, L., Parmentier, F.-J., Elberling, B., and Hansen, B. U.: GeoBasis – Guidelines and sampling procedures for the
geographical monitoring programme of Nuuk Basic in Kobbefjord, in: Nuuk Ecological Research Operations, edited by: Rasmussen, K. K., Asiaq-Greenland Survey, Nuuk, Greenland, 61 pp., available at: https://g-e-m.dk/fileadmin/g-e-m/GEM/GeoBasis_Manual_Web_2020_Nuuk_v2.pdf (last access: 15 July 2021), 2020. a, b
Richards, J. A.: Remote sensing with imaging radar, in: Signals and Communication Technology, edn. number 1, vol. 1, Springer, 361 pp., ISBN 978-3-642-02020-9, https://doi.org/10.1007/978-3-642-02020-9, 2009. a
Rittger, K., Bair, E. H., Kahl, A., and Dozier, J.: Spatial estimates of snow
water equivalent from reconstruction, Adv. Water Resour., 94,
345–363, https://doi.org/10.1016/j.advwatres.2016.05.015, 2016. a
Salvatori, R., Plini, P., Giusto, M., Valt, M., Salzano, R., Montagnoli, M.,
Cagnati, A., Crepaz, G., and Sigismondi, D.: Snow cover monitoring with
images from digital camera systems, Ital. J. Remote Sens., 43, 137–145,
https://doi.org/10.5721/ItJRS201143211, 2011. a
Skov, K., Sigsgaard, C., Mylius, M. R., and Lund, M.: GeoBasis – Guidelines
and sampling procedures for the geographical monitoring programme of
Zackenberg Basic, in: Zackenberg Ecological Research
Operations, edited by: Skov, K., Department of Bioscience, Aarhus University and Department of Geosciences and Natural Resource
Management, University of Copenhagen, 186 pp., available at: https://g-e-m.dk/fileadmin/g-e-m/Zackenberg/GeoBasis_Manual_Comp2019.pdf (last access: 15 July 2021), 2019. a, b, c, d, e
Small, D.: Flattening gamma: Radiometric terrain correction for SAR
imagery, IEEE T. Geosci. Remote, 49, 3081–3093, 2011. a
Small, D., Rohner, C., Miranda, N., Rüetschi, M., and Schaepman, M. E.:
Wide-Area Analysis-Ready Radar Backscatter Composites, IEEE T.
Geosci. Remote, 60, 1–14, https://doi.org/10.1109/TGRS.2021.3055562,
2021. a
Snapir, B., Momblanch, A., Jain, S. K., Waine, T. W., and Holman, I. P.: A
method for monthly mapping of wet and dry snow using Sentinel-1 and
MODIS: Application to a Himalayan river basin, Int. J.
Appl. Earth Obs., 74, 222–230,
https://doi.org/10.1016/j.jag.2018.09.011, 2019. a, b, c
Solberg, R., Koren, H., Amlien, J., Malnes, E., Schuler, D. V., and Orthe,
N. K.: The development of new algorithms for remote sensing of snow
conditions based on data from the catchment of Øvre Heimdalsvatn and the
vicinity, in: The subalpine lake ecosystem, Øvre Heimdalsvatn, and its
catchment: local and global changes over the last 50 years, edited by: Brittain, J. E. and Borgstrøm, R., Springer, 35–46, ISBN 978-90-481-9388-2, https://doi.org/10.1007/978-90-481-9388-2_4, 2010. a
Storvold, R. and Malnes, E.: Snow covered area retrieval using Envisat ASAR wideswath in mountainous areas, IGARSS 2004, 2004 IEEE International Geoscience and Remote Sensing Symposium, 3, 1845–1848, https://doi.org/10.1109/IGARSS.2004.1370697, 2004. a, b
Thakur, P. K., Garg, V., Nikam, B. R., Singh, S., Jasmine, Chouksey, A., Dhote, P. R., Aggarwal, S. P., Chauhan, P., and Kumar, A. S.: Snow Cover and Glacier
Dynamics Study Using C- and L-Banc SAR Datasets in Parts of
North West Himalaya, International Archives of the Photogrammetry,
Remote Sensing & Spatial Information Sciences, XLII-5, 375–382, https://doi.org/10.5194/isprs-archives-XLII-5-375-2018, 2018. a, b
Thakur, P. K., Aggarwal, S., Arun, G., Sood, S., Kumar, A. S., Mani, S., and
Dobhal, D.: Estimation of snow cover area, snow physical properties and
glacier classification in parts of Western Himalayas using C-Band
SAR Data, J. Indian Soc. Remote, 45, 525–539, 2017. a
Tsai, Y.-L. S., Dietz, A., Oppelt, N., and Kuenzer, C.: A Combination of
PROBA-V/MODIS-Based Products with Sentinel-1 SAR Data for
Detecting Wet and Dry Snow Cover in Mountainous Areas, Remote
Sensing, 11, 1904, https://doi.org/10.3390/rs11161904, 2019a. a, b
Tsai, Y.-L. S., Dietz, A., Oppelt, N., and Kuenzer, C.: Wet and Dry Snow
Detection Using Sentinel-1 SAR Data for Mountainous Areas with
a Machine Learning Technique, Remote Sensing, 11, 895, https://doi.org/10.3390/rs11080895,
2019c. a, b, c, d
Tsai, Y.-L. S., Klein, I., Dietz, A., and Oppelt, N.: Monitoring
Large-Scale Inland Water Dynamics by Fusing Sentinel-1 SAR
and Sentinel-3 Altimetry Data and by Analyzing Causal Effects of
Snowmelt, Remote Sensing, 12, 3896, https://doi.org/10.3390/rs12233896, 2020. a, b, c
Týč, M. and Gohlke, C.: imreg_dft, available at:
https://imreg-dft.readthedocs.io/en/latest/ (last access: 15 June 2021), 2014. a
Westergaard-Nielsen, A., Lund, M., Pedersen, S. H., Schmidt, N. M., Klosterman,
S., Abermann, J., and Hansen, B. U.: Transitions in high-Arctic vegetation
growth patterns and ecosystem productivity tracked with automated cameras
from 2000 to 2013, Ambio, 46, 39–52, https://doi.org/10.1007/s13280-016-0864-8, 2017. a, b, c, d, e, f, g
Young, K. L., Brown, L., and Labine, C.: Snow cover variability at Polar
Bear Pass, Nunavut, Arctic Science, 4, 669–690, 2018. a
Short summary
In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic aperture radar time series to capture the small-scale heterogeneity of snow cover (SC) and snowmelt. Thereby, we can identify start of runoff and end of SC as well as perennial snow and SC extent during melt with high spatiotemporal resolution. Hence, our approach could support monitoring of distribution patterns and hydrological cascading effects of SC from the catchment scale to pan-Arctic observations.
In this paper, we present a threshold and a derivative approach using Sentinel-1 synthetic...
